The current state of the art for optical image stabilization involves interpreting a signal on one frame of reference (the camera) relative to another frame of reference (the image being captured) and applying compensating motions (horizontal/vertical translations, or tilting) to the optical elements to counter relative motions between the camera frame of reference and the image being captured. This is generally accomplished using an angular velocity or gyroscopic sensor rigidly mounted to the camera, which provides a signal indicative of rotation of the camera frame of reference. Various algorithms carrying out processes such as integration and scaling are applied to the gyroscope signal. The result yields the direction and magnitude response signal which is applied to the active elements of the camera (motor, piezo, piston, etc.) to move the optical elements of the camera. The current state of the art devices commonly use a voice coil motor to move the optical elements. An optical element is rotated or translated based on the response signal, with the intention of counteracting the motions of the camera relative to the image such that the image remains fixed on the camera sensor during the exposure duration.
Some OIS methods use single or multi-axis gyroscopes mounted to the camera system, in the camera sensor frame of reference, with the aim of compensating for hand-shake or tremor on the part of the camera user. Such methods are generally sufficient for stabilizing images where the image itself is stationary or inherently stable. However, if the image is itself subject to motion, these methods are inadequate. Another disadvantage of current OIS methods is saturation, which can occur for motions of greater magnitude and frequency, for example those encountered in moving vehicles as opposed to in relatively static situations.
Another distinct category of methods in the prior art for image stabilization is electronic image stabilization (EIS) or digital/software based compensation, although it is important to note that these techniques are not truly “optical” image stabilization. These methods use software algorithms to post process a previously captured image or video. Since software based methods do not move any of the optical elements, they cannot correct image blur caused by motion across pixels of the sensor during the exposure and are thus inferior to true optical image stabilization.
The need therefore remains for methods and systems for true optical image stabilization that can compensate adequately for motions in situations where both the camera and the image may have independent motions, thereby causing image blur. Such methods and systems would be particularly desirable for stabilizing video for in-vehicle video conferencing, especially if they can encompass large and high frequency motions.